期刊
NEUROPSYCHOPHARMACOLOGY
卷 46, 期 1, 页码 20-32出版社
SPRINGERNATURE
DOI: 10.1038/s41386-020-00805-6
关键词
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资金
- K01 from the National Institute on Drug Abuse [K01DA047417]
- F30 from the National Institute of Mental Health [F30 MH118871-01]
- John D. and Catherine T. MacArthur Foundation
- ISI Foundation
- Paul G. Allen Family Foundation
- Army Research Laboratory [W911NF-10-2-0022]
- Army Research Office [Falk W911NF-18-1-0244, Bassett-W911NF-14-1-0679, Grafton-W911NF-16-1-0474]
- National Science Foundation [BCS1631550, PHY-1554488, NCS-FO-1926829]
- National Institute of Child Health and Human Development [1R01HD086888-01]
- National Institute of Mental Health [2-R01-DC-009209-11, R01-MH112847, R01-MH107235, R21-M MH-106799, R01-MH-116920]
Neuropsychopharmacology focuses on studying the intricate relationships between the brain, human behavior, and symptoms of illness, using computational models to understand perturbations and their effects on these systems. Through discussions and evaluations of three models, there is a call to interlink data analysis, computational modeling, and formal theory for future endeavors.
Neuropsychopharmacology addresses pressing questions in the study of three intertwined complex systems: the brain, human behavior, and symptoms of illness. The field seeks to understand the perturbations that impinge upon those systems, either driving greater health or illness. In the pursuit of this aim, investigators often perform analyses that make certain assumptions about the nature of the systems that are being perturbed. Those assumptions can be encoded in powerful computational models that serve to bridge the wide gulf between a descriptive analysis and a formal theory of a system's response. Here we review a set of three such models along a continuum of complexity, moving from a local treatment to a network treatment: one commonly applied form of the general linear model, impulse response models, and network control models. For each, we describe the model's basic form, review its use in the field, and provide a frank assessment of its relative strengths and weaknesses. The discussion naturally motivates future efforts to interlink data analysis, computational modeling, and formal theory. Our goal is to inspire practitioners to consider the assumptions implicit in their analytical approach, align those assumptions to the complexity of the systems under study, and take advantage of exciting recent advances in modeling the relations between perturbations and system function.
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